Auto-adaptive systems have many applications. These applications include event recognition based on data measured over a number of successive time periods. Events take many different forms. For example, events may include detection of a target in a particular area, sensing of an out-of-specification condition in a physical process environment or correspondence of processed psychometric measurements with a particular behavior prediction profile. Anomaly sensing is often an element of detecting an event. Event recognition may also comprise evaluation of sensed data to recognize or reject existence of conditions indicated by the data or to initiate a particular action.
Signals are produced in response to sensor outputs, and the signals are processed. A number of processing stages may be used. Processing results are used, for example, in known functions for calculating, predicting and imputing values, updating learned functions, assigning plausibility to measurements, discerning deviance between measured and expected values and event detection. Commonly, a display is used to convey information to a user.
Commonly assigned, copending U.S. patent application Ser. No. 11/484,874 (the '874 application), which is incorporated by reference herein in its entirety, provides, inter alia, significant improvements in processing capacity for auto-adaptive networks that may be embodied in field programmable gate arrays (FPGAs). Consequently, powerful anomaly detection techniques may be practiced on portable systems that are remote from a base station. One example of a portable system is an unmanned aerial vehicle (UAV).
The '874 application provides a context that comprehends a method of forecasting future feature values for a cell of a data array and a processor system wherein an array of data collected over successive time slices is processed, with a plurality of data points being provided during each time slice, a number of future time slices is specified for which to forecast a feature value, each future time slice being located a number of time slices into the future from a current reference point; a number of forecasts per feature is specified; a set of past time slices to be utilized for each prediction is defined, each said past time slice being located a number of time slices in the past away from the current reference point corresponding to a number or time slices away from the current reference point of a future time slice and defining a number of features for each data point.